Development and external validation of an artificial intelligence model for predicting mortality and prolonged ICU stay in postoperative critically ill patients: a retrospective study.

ABSTRACT:

Background

Existing predictive models in critical care, specifically for postoperative critically ill patients, often struggle to accurately predict prolonged intensive care unit (ICU) stays, a key aspect of patient care. The integration of artificial intelligence (AI) offers a promising approach for bridging this gap. We aimed to develop an AI-based model to predict mortality and prolonged ICU stay in postoperative critically ill patients, enhance prognostic accuracy, and address the shortcomings of current models.

Methods

This retrospective study included data from 6,029 postoperative critically ill patients from two medical centers, including a wide range of clinical, surgical, and laboratory variables. Multiple machine-learning models, including extreme gradient boosting

SUBMITTER: Park DJ 

PROVIDER: S-EPMC12523136 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature